





"""simple docstring"""
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    int			):
 lowerCAmelCase    				= word.split()

 def justify(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    int			) -> str:
  lowerCAmelCase    				= max_width - width
  lowerCAmelCase    				= len(_UpperCAmelCase			)
  if len(_UpperCAmelCase			) == 1:
   # if there is only word in line
   # just insert overall_spaces_count for the remainder of line
   return line[0] + " " * overall_spaces_count
  else:
   lowerCAmelCase    				= words_count - 1
   # num_spaces_between_words_list[i] : tells you to insert
   # num_spaces_between_words_list[i] spaces
   # after word on line[i]
   lowerCAmelCase    				= spaces_to_insert_between_words * [
       overall_spaces_count // spaces_to_insert_between_words
   ]
   lowerCAmelCase    				= (
       overall_spaces_count % spaces_to_insert_between_words
   )
   # distribute spaces via round robin to the left words
   for i in range(_UpperCAmelCase			):
    num_spaces_between_words_list[i] += 1
   lowerCAmelCase    				= []
   for i in range(_UpperCAmelCase			):
    # add the word
    aligned_words_list.append(line[i]			)
    # add the spaces to insert
    aligned_words_list.append(num_spaces_between_words_list[i] * ' '			)
   # just add the last word to the sentence
   aligned_words_list.append(line[-1]			)
   # join the aligned words list to form a justified line
   return "".join(_UpperCAmelCase			)

 lowerCAmelCase    				= []
 lowerCAmelCase    				= []
 lowerCAmelCase    				= 0
 for word in words:
  if width + len(_UpperCAmelCase			) + len(_UpperCAmelCase			) <= max_width:
   # keep adding words until we can fill out max_width
   # width = sum of length of all words (without overall_spaces_count)
   # len(word) = length of current word
   # len(line) = number of overall_spaces_count to insert between words
   line.append(_UpperCAmelCase			)
   width += len(_UpperCAmelCase			)
  else:
   # justify the line and add it to result
   answer.append(justify(_UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase			)			)
   # reset new line and new width
   lowerCAmelCase				,lowerCAmelCase    				= [word], len(_UpperCAmelCase			)
 lowerCAmelCase    				= max_width - width - len(_UpperCAmelCase			)
 answer.append(' '.join(_UpperCAmelCase			) + (remaining_spaces + 1) * ' '			)
 return answer


if __name__ == "__main__":
       from doctest import testmod

       testmod()






"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
